RT Journal Article
T1 Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry
A1 Rybakowska, Paulina
A1 Alarcón-Riquelme, Marta E.
A1 Marañón, Concepción
K1 Flow cytometry
K1 Mass cytometry
K1 Bioinformatics
K1 Computational tools
K1 Single-cell proteomics
K1 Data accuracy
K1 Citometría de flujo
K1 Biología computacional
K1 Algoritmos
K1 Células
K1 Exactitud de los datos
AB High-dimensional, single-cell cell technologies revolutionized the way to study biological systems, and polychromatic flow cytometry (FC) and mass cytometry (MC) are two of the drivers of this revolution. As up to 30-50 dimensions respectively can be measured per single-cell, they allow deep phenotyping combined with cellular functions studies, like cytokine production or protein phosphorylation. In parallel, the bioinformatics field develops algorithms that are able to process incoming data and extract the most useful and meaningful biological information. However, the success of automated analysis tools depends on the generation of high-quality data. In this review we present the most recent FC and MC computational approaches that are used to prepare, process and interpret high-content cytometry data. We also underscore proper experimental design as a key step for obtaining good quality data.
PB Elsevier
YR 2020
FD 2020-03-31
LK http://hdl.handle.net/10668/3683
UL http://hdl.handle.net/10668/3683
LA en
NO Rybakowska P, Alarcón-Riquelme ME, Marañón C. Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry. Comput Struct Biotechnol J. 2020 Mar 31;18:874-886
DS RISalud
RD Apr 6, 2025